multi-view

GenRecon introduces a method for 3D scene reconstruction that integrates generative 3D priors with multi-view image conditioning, achieving high-fidelity, editable mesh reconstructions of indoor environments and outperforming existing methods by 16%.

This paper introduces three parameter-efficient methods for multi-view proficiency estimation on the Ego-Exo4D dataset, shifting from discriminative classification to generative feedback. The proposed models achieve state-of-the-art accuracy with significantly fewer parameters and training epochs than video-transformer baselines.

MultiWorld is a unified framework for multi-agent multi-view video world modeling that achieves accurate control of multiple agents while maintaining multi-view consistency through a Multi-Agent Condition Module and Global State Encoder.